The present embodiments relate to metadata in storage systems and networks. More specifically, the embodiments relate to management of metadata by enabling sequential access for data scans on files with metadata.
An increased reliance on data objects has led to a need for detailed information related to the data objects, known as metadata, as well as techniques for managing and controlling the metadata. For instance, there is a high demand for images, videos, and audio. Accordingly, there is a high demand for metadata about the images, videos and audio.
To date, metadata has been applied in limited contexts, e.g. to allow manually annotation of image data shared via a social network. The limitations give rise to a technical gap between these conventional user-mediated metadata applications and the restrictive, regimented constraints imposed by data storage, management, and/or processing environments common to high throughput data processing centers, high volume data storage solutions, and related systems that operate using large volumes of data, high-volume data processing operations, an/or related data storage and retrieval solutions.
Access to metadata provides users with large quantities of information. However, accessing the metadata leads to massive scans of data that require large resources and time to process the request. Traditional file systems operate by storing metadata files independently on disk without any common write-placement patterns. Therefore, when accessing the metadata, files are treated as random access in the underlying file system thereby leading to sub-optimal scan performance.
Enterprises and organizations are creating, analyzing and keeping more data than ever before. Those that can deliver insights faster while managing rapid infrastructure growth are the leaders in their industry. To deliver those insights, an organization's underlying storage must support both new-era big data and traditional applications with security, reliability and high-performance. To handle massive unstructured data growth, the solution must scale seamlessly while matching data value to the capabilities and costs of different storage tiers and types. Consequently, it remains desirable for a high-performance solution for managing data at scale with the distinctive ability to perform archive and analytics.